| bicm {backbone} | R Documentation |
bicm estimates cell probabilities under the bipartite configuration model
bicm(M, tol = 1e-08, max_steps = 200, ...)
M |
matrix: a binary matrix |
tol |
numeric, tolerance of algorithm |
max_steps |
numeric, number of times to run loglikelihood_prime_bicm algorithm |
... |
optional arguments |
Given a binary matrix M, the Bipartite Configuration Model (BiCM; Saracco et. al. 2015) returns a valued matrix
B in which Bij is the approximate probability that Mij = 1 in the space of all binary matrices with
the same row and column marginals as M. The BiCM yields the closest approximations of the true probabilities
compared to other estimation methods (Neal et al., 2021), and is used by sdsm() to extract the backbone of
a bipartite projection using the stochastic degree sequence model.
matrix: a matrix of probabilities
Saracco, F., Di Clemente, R., Gabrielli, A., & Squartini, T. (2015). Randomizing bipartite networks: The case of the World Trade Web. Scientific Reports, 5, 10595. doi: 10.1038/srep10595
Neal, Z. P., Domagalski, R., and Sagan, B. (2021). Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections. Scientific Reports. doi: 10.1038/s41598-021-03238-3
M <- matrix(rbinom(25,1,0.5),5,5) #A random bipartite graph bicm(M)